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DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection. (arXiv:2301.03368v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
Our increasingly connected world continues to face an ever-growing amount of
network-based attacks. Intrusion detection systems (IDS) are an essential
security technology for detecting these attacks. Although numerous machine
learning-based IDS have been proposed for the detection of malicious network
traffic, the majority have difficulty properly detecting and classifying the
more uncommon attack types. In this paper, we implement a novel hybrid
technique using synthetic data produced by a Generative Adversarial Network
(GAN) to use as input for training a …
attack attacks binary data detection gan hybrid ids intrusion intrusion detection machine machine learning malicious network network traffic novel security security technology synthetic synthetic data systems technology traffic types uncommon world